I’m familiar with the general ways of teaching a chatbot: broadly leveraging NLP/NLU and parsing sentiment, sentence/intents, dialogue etc, but I’ve always found that most ML based techniques are just rote-learning on steroids and I don’t like that.
Time and time again, programmers are told how toddlers can outsmart state-of-the-art ML algorithms and that the only way to outpace this crawler is to nuke their computer with billions of data-points. Upgrade to a new graphics card. Buy a new Intel multi-core processor. Train through a VM on AWS. The list is endless…
Yet the suggestions are still dumb. We should be focusing on understanding the fundamental rules this data obliges in an open-context rather than dissecting the individual pen-strokes of the number ‘9’ in MNIST. Sure low-level insight is needed to interpret stimuli, but the scope of ML needs to grow from simple, closed-based arenas to the battlefronts of the real-world where information is randomly distributed and spontaneous.
To make the first step into this gunfire, we need to change how machines ‘think’.